In a recent paper on SER, the authors proposed a convolutional autoencoder that employs a pre-trained autoencoder and a convolutional neural network, and a triplet loss in the autoencoder is used for metric learning [1]}. Further on, a contrastive loss was used for metric learning in a Siamese network [2]}. On similar lines, a class specific triplet-loss based LSTM neural network was proposed for SER [3]}. A combination of the centre-loss, that clusters members of a class together, and the cross-entropy loss was investigated to better cluster features [4]}, [5]}. The authors proposed multiple f-similarity preserving losses for metric learning using soft labels and tested it on classification [6]}.
